Deep learning significantly improves the quality of computer vision tasks with a large amount of training data.
However, collecting real-world data is often costly and time-consuming.
Therefore, many works instead target synthesizing realistic data to provide sufficient data for training.
Barron et al.  [1]} proposed to synthesize defocus images by a layer-wised rendering scheme with a z-buffer.
Wadhwa et al.  [2]} combine a person segmentation mask with a depth map calculated from a dual-pixel camera to generated shallow depth-of-field images.
Gur et al.  [3]} use a differentiable point spread function, namely the PSF convolution layer, to synthesize realistic defocus images and train a depth from defocus network to generate depth maps.
Herrmann et al.  [4]} proposed a learning-based approach for autofocus and recently provided a real dataset for training.
However, the dataset was not open to the public at the submission time.
